The Intellectual Journal of Science and Engineering Practices, ISSN XXXX-XXXX, publishes advanced theories and practices on science and engineering technologies. Science and engineering practices are the core skills and behaviors scientists and engineers use to investigate the natural world and design solutions. The disciplinary knowledge embeds and informs these practices, which are not separate from the content of science and engineering. The Next Generation Science Standards (NGSS) identify eight key science and engineering practices. Here's a more detailed look:

1. Asking Questions (for science) and Defining Problems (for engineering): Science begins with a question about a phenomenon, while engineering begins with a problem to solve. These questions and problems are crucial for guiding research and problem-solving. 

2. Developing and Using Models: Science and engineering often involve creating models to represent complex systems or phenomena. These models can be physical, conceptual, or mathematical and are used to explore, test, and communicate ideas.

3. Planning and Carrying Out Investigations: Scientists and engineers plan and conduct investigations to gather evidence and test hypotheses or design solutions. This involves designing experiments, collecting data, and making observations. 

4. Analyzing and Interpreting Data: Data analysis is a critical step in both science and engineering, allowing scientists and engineers to draw meaningful conclusions from their investigations. 

5. Using Mathematics and Computational Thinking: Mathematical and computational tools are essential for representing variables, making predictions, and analyzing data.

6. Constructing Explanations (for science) and Designing Solutions (for engineering): Science aims to construct explanations for natural phenomena, while engineering aims to design solutions to specific problems. 

7. Engaging in Argument from Evidence: In science, reasoning and argument are essential for evaluating claims and finding the best explanations. In engineering, arguments from evidence are used to justify design choices. 

8. Obtaining, Evaluating, and Communicating Information: Scientists and engineers need to be able to gather information from various sources, evaluate its reliability, and communicate their findings effectively.

The Intellectual Journal of Machine Learning and Modern Computing, ISSN XXXX-XXXX, publishes advanced theories and practices in machine learning and modern computing areas. Machine learning (ML) is a subset of artificial intelligence (AI) that enables computers to learn from data without explicit programming. It's a key component of modern computing, transforming how we approach data analysis and problem-solving. ML algorithms analyze data, identify patterns, and make predictions or decisions, often in real time. The relationship between machine learning and modern computing is as follows:

• Revolutionizing Data Analysis: ML enables computers to analyze vast amounts of data and identify complex patterns that would be difficult for humans to detect. 
• Enabling Intelligent Systems: ML is the foundation for building intelligent systems like virtual assistants, chatbots, and self-driving cars. 
• Improving Efficiency and Productivity: ML can automate tasks, streamline processes, and free up human resources to focus on more complex issues. 
• Transforming Industries: ML is impacting various industries, including energy, finance, healthcare, and transportation, by enabling data-driven decision-making. 
• Advancing Research and Innovation: ML provides new tools and techniques for research and innovation, opening up new avenues for exploring complex problems.

In essence, machine learning is a powerful tool that is transforming modern computing by enabling computers to learn, adapt, and make intelligent decisions based on data.

The Intellectual Journal of Intelligent and Autonomous Systems, ISSN XXXX-XXXX, publishes advanced theories and practices on machine intelligence and advanced autonomous technologies. Intelligent Autonomous Systems (IAS) combine machine intelligence and advanced technologies to create systems that can perceive, reason, learn, and control their environment. Designed to perform tasks autonomously, the systesm can operate without constant human supervision. IAS research explores the nature of intelligence in areas like perception, reasoning, learning, and control, aiming to develop and implement these theories in real-world applications. The main keys to intelligent autonomous systems are

• Intelligence: IAS leverage AI techniques like machine learning and pattern recognition to understand and react to their surroundings.
• Autonomy: can operate independently, making decisions and taking actions without continuous human input.
• Perception: use sensors and other means to gather information about their environment.
• Reasoning and Learning: can analyze data, learn from experience, and make decisions based on their understanding of the situation.
• Control: can execute actions to achieve their goals, such as controlling robots, vehicles, or other systems.

The Intellectual Journal of Image Processing and Computer Vision, ISSN XXXX-XXXX is dedicated to publishing advanced theories and practices related to image processing and computer vision. These two distinct fields are interconnected by the broader domains of artificial intelligence and pattern recognition. Image processing is primarily concerned with the enhancement and manipulation of images, while computer vision aims to extract meaningful information and understanding from those images, such as the identification of objects or the understanding of scenes. In computer vision applications, image processing techniques are frequently employed as essential preliminary steps. Examples of image pre-processing techniques that are employed to prepare images for computer vision algorithms include noise reduction and contrast adjustment. Essentially, image processing is concerned with enhancing the aesthetics of images, whereas computer vision is dedicated to allowing computers to "see" and understand the content of those images.

The book series Mechanisms in Electrical Engineering (MEE), ISSN XXXX-XXXX rapidly publishes cutting-edge advancements in electrical engineering with a focus on maintaining excellent quality. Although MEE primarily focuses on original research presented in monographs, we also welcome authors to submit publications that are specifically designed to aid student education and professional training in many areas of electrical engineering. The series encompasses a wide range of subjects, including traditional & cutting-edge areas such as artificial intelligence, biomedical devices, MEMS & NEMS, circuits & systems, communication engineering, control systems & engineering, cybersecurity, electronics engineering & microelectronics, electro-optical engineering, energy systems, information theory & networks, instrumentation engineering, internet of things, microelectronics, power electronics & electrical machines, power engineering; signal, image, speech & video processing, telecommunications engineering, and wireless & mobile communication.